Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes

Rostami-Tabar, Bahman ORCID:, Babai, M. Zied, Ali, Mohammad and Boylan, John E. 2019. The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes. European Journal of Operational Research 273 (3) , pp. 920-932. 10.1016/j.ejor.2018.09.010

[thumbnail of Rostami-Tabar et al.pdf]
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview


Various approaches have been considered in the literature to improve demand forecasting in supply chains. Among these approaches, non-overlapping temporal aggregation has been shown to be an effective approach that can improve forecast accuracy. However, the benefit of this approach has been shown only under single exponential smoothing (when it is a non-optimal method) and no theoretical analysis has been conducted to look at the impact of this approach under optimal forecasting. This paper aims to bridge this gap by analysing the impact of temporal aggregation on supply chain demand and orders when optimal forecasting is used. To do so, we consider a two-stage supply chain (e.g. a retailer and a manufacturer) where the retailer faces an autoregressive moving average demand process of order (1,1) -ARMA(1,1)- that is forecasted by using the optimal Minimum Mean Squared Error (MMSE) forecasting method. We derive the analytical expressions of the mean squared forecast error (MSE) at the retailer and the manufacturer levels as well as the bullwhip ratio when the aggregation approach is used. We numerically show that, although the aggregation approach leads to an accuracy loss at the retailer's level, it may result in a reduction of the MSE at the manufacturer level up to 90% and a reduction of the bullwhip effect in the supply chain that can reach up to 84% for high lead-times.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0377-2217
Date of First Compliant Deposit: 18 September 2018
Date of Acceptance: 6 September 2018
Last Modified: 12 Nov 2023 20:05

Citation Data

Cited 9 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics